Dynamic semiparametric factor models in risk neutral density estimation

Arbeitspapier

Dimension reduction techniques for functional data analysis model and approximate smooth random functions by lower dimensional objects. In many applications the focus of interest lies not only in dimension reduction but also in the dynamic behaviour of the lower dimensional objects. The most prominent dimension reduction technique - functional principal components analysis - however, does not model time dependences embedded in functional data. In this paper we use dynamic semiparametric factor models (DSFM) to reduce dimensionality and analyse the dynamic structure of unknown random functions by means of inference based on their lower dimensional representation. We apply DSFM to estimate the dynamic structure of risk neutral densities implied by prices of option on the DAX stock index.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: SFB 649 Discussion Paper ; No. 2008,038

Classification and Topics

• Classification: Wirtschaft ; Semiparametric and Nonparametric Methods: General ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Asset Pricing; Trading Volume; Bond Interest Rates
• Subject: Dynamic factor models ; dimension reduction ; risk neutral density ; Faktorenanalyse ; Nichtparametrisches Verfahren ; Dynamisches Modell ; Theorie ; Schätzung ; Aktienoption ; Risikoneutralität ; Statistische Verteilung ; Deutschland

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Giacomini, Enzo ; Härdle, Wolfgang Karl ; Krätschmer, Volker
• Event: Veröffentlichung
• (who): Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
• (where): Berlin
• (when): 2008

Further information

• Handle: http://hdl.handle.net/10419/25280
• Last update: 10.03.2025, 11:45 AM CET